PHENOMENOLOGICAL ARCHITECTURE OF A MIND AND …journalpsyche.org/articles/0xc037.pdf · machine consciousness, and diagnosis of dynamic brain diseases will be discussed briefly. ...

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Below is the unedited draft of the article that has been accepted for publication (© New Mathematics and Natural Computation, 2009, V. 5. No 1. P. 221–244)

PHENOMENOLOGICAL ARCHITECTURE OF A MIND AND OPERATIONAL ARCHITECTONICS OF THE BRAIN: THE UNIFIED METASTABLE CONTINUUM

ANDREW A. FINGELKURTS, ALEXANDER A. FINGELKURTS, CARLOS F.H. NEVES

BM-Science – Brain and Mind Technologies Research Centre, P.O. Box 77, FI-02601, Espoo, Finland

In our contribution we will observe phenomenal architecture of a mind and operational architectonics of the brain and will show their intimate connectedness within a single integrated metastable continuum. The notion of operation of different complexity is the fundamental and central one in bridging the gap between brain and mind: it is precisely by means of this notion that it is possible to identify what at the same time belongs to the phenomenal conscious level and to the neurophysiological level of brain activity organization, and what mediates between them. Implications for linguistic semantics, self-organized distributed computing algorithms, artificial machine consciousness, and diagnosis of dynamic brain diseases will be discussed briefly. Keywords: EEG, brain operation; functional isomorphism; dynamical neuroscience; consciousness

1. Introduction

Modern neuroscience, which is based on the conceptual resources of dynamical system theory

and the methodological principles of complex system science, began about three decades ago,

when Walter Freeman, the giant of 20th century neuroscience and one of the architects of the

dynamical system framework in neuroscience, has produced a steady flow of studies of the

dynamic principles of wave patterns, attractors, bifurcations, and critical phase transitions in

brains.1-10 These and some other studies (for the review see Ref. 11) have yielded numerous

relevant findings, which help to establish a basis for formulating the relation between neural and

mental events on a common basis.

Freeman’s classic experiments12,13 measuring electroencephalographic (EEG) activity within

the olfactory bulb of rabbits and cats demonstrated that information concerning the identity of a

particular odour was not carried by the temporal shape of any particular EEG wave, but by the

spatial pattern of EEG amplitude across the entire surface of the olfactory bulb. These data lead

to building a model of a hierarchy of ‘neural masses’,14 which describes increasing complexity of

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structure and dynamical behavior of brain process.15,16 The basic building blocks of this model

are K-sets – topological specifications of the hierarchy of connectivity in neuron populations in

the 6-layer cortex. The K0 set describes the dynamics of a cortical micro-column (~10 thousand

neurons). A KI set includes K0 sets from a given cortical layer with specific properties.

Similarly, KII includes KI sets from different populations (i.e., excitatory and inhibitory ones).

KIII subsumes several KII sets modeling various cortical areas and KIV already covers many

cortical areas across the hemisphere. The highest level of neocortex hierarchy is described by

KV.

Neurophysiological observations made by Freeman over several decades contribute to the

idea that brains are essentially non-equilibrium systems which do not come to a steady state even

for a fraction of second. Brains constantly change, using dynamical patterns of activation in their

operation to present memories, concepts, and actions.17

Indeed, as it is shown in the recent review by Bressler (see Ref. 18), cortex dynamically

generates global neuro-cognitive states from interactions among its areas using local and remote

patterning within the cortex, whereas each cortical area is a relatively autonomous entity and has

a unique pattern of interconnectivity with other cortical areas. These considerations suggest that

brain integrative functions are the result of competition of complementary tendencies of

cooperative integration and autonomous fragmentation among many distributed areas.19,20 The

interplay of these two tendencies (autonomy and integration) constitutes the metastable regime of

brain functioning,21 whereas local (autonomous) and global (integrated) processes coexist as a

complementary pair, not as conflicting principles.22

Following Freeman’s model of hierarchy of ‘neural masses’ and Kelso’s metastable principle

of brain functioning, we would like to propose in this article that mind phenomenological

architecture and brain operational architectonics represent the complementary aspects of the

same unified metastable continuum. We will show that metastability introduces the hierarchical

coupling between the brain and mind while simultaneously allowing them to retain their

individuality.

2. Mind Phenomenological Architecture

There have been numerous attempts to describe the phenomenological properties of mind (for

the review, see Ref. 23). Literally, phenomenology refers to “phenomena”: appearances of

things, or things as they appear in our experience, or the ways we experience things.24 William

James was the first scientist, who did the most fecund and deep (even though metaphorical at

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places) phenomenological description of the structure of human consciousness in his ‘Stream of

Though’ essay.25 His main observation that consciousness is dynamic where it continually moves

from one relatively stable thought to another relatively stable thought is currently being

developed by various authors.26-29

The most recent and the most detailed analysis of phenomenological architecture of

consciousness has been done by neuroscientist Antti Revonsuo in his book ‘Inner Presence’.30 In

the present contribution we will use his description of phenomenal world. Although a full

account of the phenomenal (virtual) world is beyond the scope of this article, we will illustrate

this world by describing the most important features of it (those which are required for

instantiation of phenomenal consciousness). However, before we will go on to elaborate this

subject, it might be useful to clarify the definition of phenomenal consciousness.

It is a common place that the term “consciousness” has a number of different connotations.

We take a stand that phenomenal consciousness refers to the level of organization in the brain30

that captures all immediate and undeniable facts (phenomena) of subjective experiences

(concerning hearing, seeing, touching, feeling, embodiment, moving, and thinking) that present

to any person right now and right here. This subjective phenomenal (virtual) world consists of

the fine hierarchical architecture (as it described in Ref. 30), which we will describe below.

2.1. Properties of phenomenal consciousness (subjective virtual experience)

The following is a brief description of the properties of phenomenal consciousness, which we

borrow from Antti Revonsuo (detailed description and discussion see in Ref. 30):

• The phenomenal patterns, objects, and persons are never experienced as representations,

– they are transparent phenomenal states, – we just ‘look’ through them as if they are the

entities of the real world itself and they are immediately present and directly perceived by

us. Thus, the phenomenal patterns, objects, and persons are transparent surrogates (or

realistic mental, virtual simulations) of physical patterns, objects, and persons that they

are representative of.

• The phenomenal features of any complexity (either patterns or even objects) involve

temporal aspect: they are what is happening right now. They are present during some

subjective temporal window; they are constituents of the states of affairs in which they

occur. This state did not exist before; it endures across time and then disappears. In this

sense they are both the act (process) and the object (thing) at the same time.

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• The phenomenal level is capable of realizing an astronomical number of different

phenomenal qualities, patterns, and objects. It is thus characterized by enormous

multivariability and combinatorial capacity.

• Neither the phenomenal level as a whole nor any of its parts are somehow “perceived” by

themselves. That is, they do not serve as objects of perception, instead they are

intrinsically self-presenting (meaning that they do not need to be present to anyone).

• Phenomenal experiences constructed and updated extremely rapidly. For example, we are

able of recognizing and understanding complex images of scenery flashed briefly around

only 100 ms. In everyday life if we turn our eyes or head very rapidly, our phenomenal

experience is instantly updated to accommodate the new perspective. Thus, the temporal

resolution of phenomenal consciousness should be very high (it can completely

reorganize itself 3 to 10 times per second).

• The phenomenal field tends to retain a coherent multisensory coordination and

organization. Shape, color, location, sound, touch, and other sensory features defining a

phenomenal object are perfectly coordinated in time and unified in space (so-called

phenomenal space-time, PST).

• Subject that experiences phenomenal consciousness always feels directly present in the

center of an externalized multimodal perceptual reality, the virtual world. However, this

phenomenal world is in online resonance with external physical world (external physical

space-time, EPST), which can causally modulate the structure of phenomenal experience

through multiple sensory systems.

• The phenomenal world has immensely complex structure and fine hierarchical

organization:

o Phenomenal space – can be described as a 3D, centered, bounded volume

(coordinate system) in which each location has the capability to realize a

characteristic variety of self-presenting, qualitative features and thereby to

construct transparent surrogates or virtual objects. Importantly, the phenomenal

fields of different modalities (for example, visual and auditory) are spatially and

temporally integrated, so that the different features belonging to the same object

are realized in the same location and time (PTS).

o Phenomenal features (qualities) – can be described as simple phenomenal

contents (sounds, colors, touches, emotions, tastes, smells, and so on). They are

the identity, the “stuff” that experiences per se are made of.

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o Patterns of qualities – can be described as carefully organized qualities to make

up the patterns of experiences.

o Phenomenal objects (with their Gestalt and semantic windows) – can be described

as complex patterns of qualities which are spatially extended and bounded with

each other to form a unified item (Gestalt window) with a particular meaningful

categorization (semantic window) immediately present for the subject. Any such

object can be further organized hierarchically into parts (or features) of a more

complex object, or on the contrary be decomposed to its parts, all of each can be

realized as separate simpler virtual objects independent of each other and with

their own Gestalt and semantic windows.

o Phenomenal map – can be described as a geographical orientation which gives

understanding where is the currently present place located in relation to other

places not currently present.

o Phenomenal qualities, their patterns, and full-fledged objects should be

simultaneously present within the phenomenal space in order to establish relations

(spatial and meaningful) between each other. The phenomenal objects that are not

actualized at the moment (being preattentive or not in the focus of awareness) can

be described as raw (or candidate) objects that possess no Gestalt and semantic

windows, but rather some phenomenal undefined “stuff”.

o Attention guides the actualization of full-fledged virtual, phenomenal objects on a

one at a time basis, moving serially from one phenomenal pattern to another. This

process gives rise to stream of consciousness.

Currently in neuroscience it is agreed that the further understanding of phenomenal

consciousness will obviously rely upon the view according to which phenomenal consciousness

is grounded to material carrier processes that take place in the brain.17,31-36 It is even suggested

that phenomenal consciousness is a higher level of biological organization in the brain.30

There are several theoretical frameworks that try to integrate brain and mind.17,31,32,34,37-46

However, practically all of them do not take phenomenal consciousness of mind seriously and in

the best case they try to explain consciousness through its neural correlates,47-49 despite the fact

that ‘correlation’ is too weak relation to be definitive in any explanation.30 Another drawback of

such theories is the fact that they postulate many entities which could not be measured in practice

easily, and their experimental exploration stands as an important challenge.50 Furthermore, they

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usually do not consider dynamical and compositional nature of phenomenal world and disagree

about relevant for the consciousness level of brain organization.

Among the different methodological strategies adopted to study and describe the brain-mind

complexity and its expression in the complexity of brain activity, the so-called “Operational

Architectonics” (OA) framework35,51-55 has received attention, especially due to its good

compromise between simplicity, neurophysiologic accuracy, and cognitive plausibility. Even

though OA framework has many similarities with other theoretical conceptualizations, it is quite

distant from them in the core principles (for the detailed analysis, see Ref. 54). Additionally, in

the context of OA framework, there is a range of methodological tools which enable in practice

to measure the postulated entities of the theory.55

3. Brain Operational Architectonics

Previously (see Ref. 35, 54), we have already discussed that phenomenological constructs of

mind should be related to the dynamic operations of large-scale brain networks. It seems that this

view is supported by most researchers working in the field.17,20,31-34,56-58 Indeed, brain generates a

highly structured and dynamic extracellular electric field in spatial and temporal domains34 and

over a range of frequencies.59 This field exists within brain internal physical space-time (IPST)

and is best captured by the electroencephalogram (EEG) measurement.60 Studies indicate that

EEG is a highly organized macro-level electrophysiological phenomenon in the brain, which

captures the operations of large-scale cortical networks and which is remarkably correlated with

both behavior and cognition.3,35,60,61,63

OA theory explores the temporal structure of information flow and the inter-area interactions

within a network of dynamical, transient, and functional neuronal assemblies (which activity is

“hidden” in the complex nonstationary structure of EEG signal; see Ref. 62) by examining

topographic sharp transition processes (on the millisecond scale) in the EEG.35,51-55 The detailed

analysis of the complex structure of hierarchical architecture of EEG reveals the particular

operational space-time (OST) which literally resides within the IPST and is isomorphic to the

PST (phenomenal space-time, see above) and, as we propose, constitutes the neurophysiological

basis of mind phenomenal architecture.35,54 Below we will illustrate this functional isomorphism

by relating the EEG structure with the structure of phenomenal consciousness as it is described

in the previous section. By definition, two systems that are functionally isomorphic are, in virtue

of this fact, different realizations of the same kind (for detailed discussion see Ref. 64). In other

words, two functionally isomorphic different systems bring about the same function that defines

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the kind. Functional isomorphism is ‘visible’ only at the level in which similarities between

otherwise disparate realizations can be seen, and so it is at this level that we must look for laws

ranging over them.

According to the OA theory, whenever any pattern of phenomenality (including reflective

thought) is instantiated, there is neurophysiological pattern of appropriate kind that corresponds

to it.65 These patterns (expressed as the virtual operational modules) are brought to existence by

joint operations of many functional and transient neuronal assemblies in the brain. The notion of

“operation”, then, is the fundamental and central one in bridging the gap between brain and

mind: it is precisely by means of this notion that it is possible to identify what at the same time

belongs to the phenomenal conscious level and to the neurophysiological level of brain activity

organization, and what mediates between them.35,52 Both, the material neurophysiological

organization that characterizes brain and the informational order that characterizes mind

necessarily involve such events as operations at their cores.

“Operation” stands for the process or series of acts/functions that applied to an operand, yield

a transform, and are limited in time;66 and can be broadly defined as the state of being in effect.67

This is so regardless of whether this process is conceptual/phenomenal or physical/biological. In

fact, everything what can be represented by a process is an operation. This provides a basis for

discussion of the relative complexity of operations, where there is a more complex

operation/operational act that subsumes the simpler ones.35,52 Understanding of the operation as a

process and considering its combinatorial nature, seems especially well suited for describing and

studying the mechanisms of how information about the objective physical entities of the external

world can be integrated, and how unified/coherent phenomenal objects or thoughts can be

presented in the internal subjective world by means of entities of distributed neuronal brain

assemblies.

Below we will analyse the main properties of brain operational architectonics, describe their

relation/isomorphism with the phenomenal properties of mind, and indicate how they can be

practically measured.

3.1. Properties of brain operational architectonics

• Brain operational architectonics has inherently complex and fine hierarchical

organization which is isomorphic to mind phenomenal architecture35,52:

o The starting point is to specify the functionality of the system at most basic, low

level. The electrical field at any point in the brain supposed to be a superposition

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of the induced fields from all of the neurons in the vicinity which is superimposed

on the fields generated by ion movement; and it depends on the firing frequency,

geometry, and the dielectric properties of neurons and the connections among

them.34 For neurons that would be arranged randomly, their induced fields will

tend to sum to zero; however, the laminar organization of the brain neocortex (and

other) structures with parallel and re-entrant loops is responsible for the 3D,

bounded and structured volume (coordinate system) in which each location

(neuron) has the potential capacity to realize and process some attribute of the

physical object or scene. The fact that neurons are able to synchronize their

subthreshold oscillations (elemental operations), leading to fixed states of an

overall neuronal assembly and to a rapid transition between such states, has been

shown experimentally and in computational models.68,69

Considering that functional isomorphism requires the functional coupling

(connectivity) between component entities of the system, their functional

organizations and their behaviors (for further detail, see Ref. 53 and 54), one may

see that the neuropil of spatially and temporally organized neurons (IPST), which

produce highly structured electrical field in the brain, is isomorphic with the

phenomenal space-time (PST; see Section 2.1.).

o The synchronization of operations executed by each neuron forms transient

functional neuronal assemblies.70-72 Neuronal assembly has defined as distributed

group of neurons or neural masses for which correlated activity persists over

substantial time interval.60 It is suggested that these time intervals are required to

accomplish basic operations57,73-75 on presenting simple phenomenal contents,

such as sounds, colors, smells, and so on.35 Here we should stress that in the

activity of neuronal assemblies, additionally to phenomenal, also the physical

(non-mental) operations are reflected.

The activity of these neuronal assemblies is ‘hidden’ in the complex

nonstationary structure of EEG signal.35,53,62 Precisely, at the EEG level the

activity of neuronal assemblies is reflected in defined periods (segments) of quasi-

stationarity within different frequency ranges (for reviews, see Ref. 35, 51, 52,

76). Indeed, EEG waves recorded from the scalp are integrated excitatory and

inhibitory post-synaptic potentials of neuronal membranes. Since they reflect

extracellular currents caused by synchronized neural activity within the local

brain volume,58 the EEG signal within quasi-stationary segments is the envelope

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of the probability of non-random coherence (so-called a ‘common mode’ or a

‘wave packet’,77 in the neuronal masses near to the recording electrode. The

segments of quasi-stationarity in EEG can be obtained using an adaptive

segmentation approach.35,51,55,61,76

One may see that operations of transient neuronal assemblies are functionally

isomorphic with phenomenal features (qualities) (see Section 2.1.). It has been

shown that a set of ‘feature extracting neural assemblies’ decompose in parallel

the complex stimulus into so-called fragments of sensation.58

o Different brain operations presenting different qualities and executed by different

neuronal assembles tend to be synchronized if they happened to be at the same

time and related to the same perceptual/cognitive object or act.35,51,54 As a result,

the metastable brain states are emerged which accompany the realization of brain

complex operations. These metastable brain states, which we call Operational

Modules (OM), constitute the new (higher) level of abstractness (OST,35,52,54).

OMs (being by themselves the result of synchronized operations going on in

distributed brain structures) could be operationally synchronized between each

other on new time scale, and thus forming more abstract and more complex OM,

which constitute new integrated experience.35,52,54 We have proposed that each of

the new OMs is not just a sum of simpler OMs, but rather is most naturally a

union of abstractions about simpler OMs.35,52,54 At the same time, the complex

OMs could be decomposed to simpler ones and all the way down until the basic

operations.

At the EEG level, the constancy and continuous existence of OMs persist across a

sequence of discrete and concatenated segments of stabilized local EEG activities

that underlie them. In this sense the OMs are metastable: intrinsic differences in

the activity between neuronal assemblies which constitute OM are sufficiently

large and each neuronal assembly does its own job, while still retaining a

tendency to be coordinated together. Simultaneous existence of autonomous and

integrated tendencies signifies the metastable principle of brain

functioning.20,21,51,53,78 In practice, the structural (or operational) synchrony

measure enables researchers to detect periods (OMs) with a more or less

generalized stabilization of quasi-stationary segments registered in the local

EEGs.35,51,55,61,76 For experimental support see Ref. 80-83.

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One may see that the level of OMs is functionally isomorphic with the organized

patterns of qualities and with the full-fledged phenomenal objects (see Section

2.1.). Operational synchrony processes among different neuronal assemblies,

located in different brain regions, serve to bind spatially dispersed phenomenal

features (bases of sensations) of a multimodal stimulus or objects into the

integrated and unified patterns of qualities and further into the phenomenal

objects35,52 with unique Gestalt and semantic windows.30 For the experimental

support see Ref. 82.

o The metastable regime of brain functioning allows simultaneous presence of

transient neuronal assemblies as autonomous entities and OMs of different

complexity (synchronized neuronal assemblies) within the same IPST of the

brain. Because of the composite polyphonic character of the electrical (EEG)

brain field, this field may be presented as a mixture of many time-scale processes

(individual frequency components).59,60,79 Consequently, a large number of

functionally distinct OMs can co-exist simultaneously on different frequencies

and even between them (for experimental support, see Ref. 61, 80, and 81).

One may see that these peculiarities of OST are functionally isomorphic with

analogous features of PST (see Section 2.1.) where phenomenal features, their

patterns and full-fledged objects are simultaneously present within the

phenomenal space.

o Attention guides the construction or decomposition of complex OMs on a one at a

time basis moving serially from one OM to another.35,52 Attention could impose

an operational synchrony threshold modulation on neural assemblies that need to

participate in the execution of a particular mental, cognitive, or behavioral act and

thereby permits rapid synchronization of selected operations within the same OM.

In this way, attention could act like a dynamic filter that accomplishes the rapid

required (un)grouping and temporal (un)binding of neural assemblies

operations.84

One may see that proposed role of attention is functionally isomorphic with the

analogous role of attention in the construction of virtual, phenomenal objects in a

serial way. This process gives rise to stream of consciousness (see Section 2.1.).

As it was discussed in our previous work,51 the succession of discrete and

relatively stable OMs which present cognitive acts, phenomenal objects, or

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thoughts separated by rapid transitive processes (abrupt changes of OMs,

cognitive acts, or thoughts). For the similar view see Ref. 17, 85.

• Transient neuronal assemblies and OMs (of any complexity) are not experienced from the

first-point of view as representations, – they are transparent brain local and global

operational states that directly present phenomenal patterns, objects, and persons (see

Section 2.1.), which in their own turn are transparent virtual surrogates of physical

patterns, objects, and persons that they are representative of.30 For a similar view see Ref.

17.

• Each transient neuronal assembly and OM (of any complexity) exists in their own OST

(operational space-time).35,54 OST is the abstract space and time ‘constructed’ by the

brain each time a particular OM emerges. The continuity of OMs exists as long as the set

of spatially distributed neuronal assemblies keeps synchronicity between their discrete

operations.54 We argue that at the phenomenological level, a continuity of consciousness

would be experienced. In this context, transient neuronal assemblies and OMs are both

the acts (processes) and the objects (things) at the same time. These features of the brain

operational architectonics are functionally isomorphic with the mind phenomenal

architecture’s features, whereas phenomenal patterns and full-fledged objects are also

both the processes and things30 (see Section 2.1.).

• Operational architectonics of the brain is characterized by enormous multivariability and

combinatorial capacity.53 It is capable of realizing practically infinite hierarchy of OMs.

On the top of such infinite hierarchy there should be the maximal OM presenting and

abstracting all the ‘underneath’ OMs for a given moment of time. These features of brain

operational architectonics are functionally isomorphic with analogous features of

phenomenal subjective experience (see Section 2.1.). Here it is worth to note that if

simple OMs presenting component elements of complex object or scene get operationally

synchronized into a new complex OM, then the complex object or scene is experienced

subjectively, but there are no experiences about component elements, since component

simple OMs are not presented anymore.35,52

• It was demonstrated experimentally that the construction of OM and their

updates/transitions appear extremely abruptly, when the set of brain areas which

constitute an OM rapidly looses functional couplings with each other and establishes new

couplings within another set of brain areas, thus demarcating a new OM in the IPST

continuum of the brain.80,83 Hence, temporal resolution of operational architecture is very

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high. The same is true for the temporal resolution of phenomenal architecture of mind30

(see Section 2.1.).

• Operational synchrony process going on among different neuronal assemblies (located in

distant brain regions) serves to bind spatially dispersed presentations of a multimodal

stimulus and/or objects into an integrated and unified percept or OM.52 Indeed, using a

robust illusion known as the ‘McGurk effect’,86 it has been shown that the crossmodal

binding in the human brain is achieved through the process of operational synchrony

between modality-specific and non-specific cortical areas, rather than in so-called

convergence regions of the cortex.82 It is interesting that the subjects who did not

experience subjectively the McGurk illusion (meaning that they were lack of conscious

multisensory integration) in contrast demonstrated significant uncoupling (negative

values of the operational synchrony) of particular brain areas (for a discussion see Ref.

82). These processes are functionally isomorphic with the multisensory coordination and

integration taking place in the phenomenal architecture of a mind (see Section 2.1.).

The description of both architectures of the phenomenal consciousness (previous Section) and

operational brain functioning (current Section) clearly shows that they are isomorphic to each

other through the shared very notion of operation. Summarizing, we can conclude that PST

literally resides within the confines of the brain, meaning that it is realized in the IPST inside the

brain. However, to understand how the PST is actually realized in the brain, we need to introduce

the intermediate level of description which would bridge the biological (physical) and

phenomenal (mental) levels of brain organization and tie these two levels ontologically together.

The electrophysiological and bioelectrical activity of the brain uses the IPST to form the new

level of abstractness – OST, which serves as a transparent surrogate of even higher level of

abstractness – PST (which is described in the previous Section). PST, in its own turn, also serves

as a transparent surrogate of the EPST of the world. If this happens to be correct, then the

phenomenal architecture of mind can, in fact, be measured by the brain operational space-time

(OST) architectonics. In this context brain operational architectonics and mind phenomenal

architecture are the complementary aspects of the same metastable continuum.

Metastability introduces four important characteristics that are described in recent article by

Kelso and Tognoli19 and should be considered by any theoretical framework, which uses

principle of metastability. First, metastability accommodates heterogeneous elements: in the

context of the present contribution, it is brain operational architectonics and mind phenomenal

architecture with their intrinsic dynamics. Second, metastability does not require a

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disengagement mechanism when the system has to switch to another state: indeed, when there is

any change in the phenomenology we observe changes in the electrophysiology.35,52-54 Third,

metastability allows the system to flexibly browse through a set of possibilities (tendencies of the

system) rather than adopting a single ‘point of view’: this is the case for both physiological and

phenomenological levels of brain organization.30 Fourth, the metastable system favors no

extremes; in other words, metastability of a system is an expression of the full complexity of this

system: indeed, mind-brain continuum is expressed through the entangled complementary

structures of electrophysiological and phenomenological phenomena in the brain.35,53

The approach formulated in this paper relates to Biological Realism (a variety of scientific

realism) that directly study the interface between neural and mental phenomena.30 According to

this philosophical view (a) consciousness exists in its own right, (b) it is natural phenomenon, (c)

it has some causal powers distinct from purely neurophysiological (non-phenomenal) realm, and

(d) it ontologically depends on brain – the spatial location of the mental phenomenon in the

natural world. If phenomenal mind is a biological level of brain organization,30 then it follows

that the structure of neurobiological phenomena (at some higher level of its organization, such as

EEG) corresponds to (or is functionally isomorphic with) the structure of the phenomenal level

itself.51,65 Hence, the presented approach is physiologically and theoretically plausible and leads

to several interesting implications for linguistic semantics, self-organized distributed computing

algorithms, artificial machine consciousness, and diagnosis of dynamic brain diseases.

4. Implications of Brain-Mind Metastable Continuum Framework

Presented in this contribution theory enables researchers to identify and eventually to start

experimentally to study the parallels between phenomenal and brain levels within the same

theoretical and methodological framework. To do so, the explanation should be made within the

terms of a phenomenon that is shared by both organizational levels of the brain

(phenomenological/mental and neurophysiological/biological). As we postulated above, it is the

concept of ‘operation’ that is the needed shared fundamental reference and which provides us

with a starting point for the conceptual integration and unified research program.

4.1. Implications for the linguistic semantics

Operational semantics (OS) theory describes and studies the nature and structure of linguistic

thought, that is, the kind of reflective thought that lies at the basis of human language, and of

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which such language is the expression.87,88 This is the only linguistic framework that explicitly

aims to describe, measure, and model the linguistic operations involved in the complex human

higher-order reflective consciousness (thoughts) which is governed by the brain.

According to OS theory, reflective/linguistic thought is always operational (made up of

operations) and OS defines explicitly the structure of linguistic thought. This structure is

characterized by correlators, correlata, and by a hierarchy of operations of increasing

complexity.87,88 One may see that OA theory of brain functioning (as it is described in Section 3)

uses the same theoretical constructs: the brain functioning is also characterized by a hierarchy of

operations of increasing complexity and functional connections between these different

operations. The basic idea is that the synchronized brain activity produced by local neuronal

assemblies is linked to the required operation (of any complexity) and only those local activities

evoked by common objects, scenes, or tasks are bound together in dynamically formed

metastable OMs.35,51-54

Thus, the operational level of linguistic thought can be studied simultaneously with the

correspondent level of brain operational architectonics and, in such a way, bridge the gap

between linguistic thought and the brain.89

4.2. Implications for self-organized distributed computing algorithms

Principles of the OA framework can be used to build the scalable applications (computing

systems) operating in dynamic context. Such system would be a set of components. There is no

need to have a dedicated decision making unit for the purpose of defining the best configuration

of the system. This is because the intelligence required for optimal adaptation should be

distributed around the system. Each component shall have some parts of the overall intelligence

and the system as a whole shall have an ability to organize its components into a working

framework without the need of external help or control.90

For example, input to the system is presented as a multimodal signal. Each modal represents

one required function. The multimodal signal (with several frequencies inbuilt in it) is distributed

among all the system components. These signals are used for setting up coalitions for tackling

external requirements, resource management, and unexpected disturbances. The signal is

activating only those components that can ‘understand’ and process a particular modal of the

broadcasted signal. Ignited/activated components also generate their own frequency signals and

might activate other components that would react to the messages.

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This is exactly the scenario by which the brain operates: the sufficient coordination of the

integrative activity of brain neuronal assemblies (located in different brain areas) is reached to

the extent that these assemblies are able to mutually influence each other in order to reach a

common functional state, stabilizing main parameters of its activity.35,51,53 However, each

specialized neuronal assembly performs a unique role by expressing its own form of information,

and at the same time its performance is largely constrained by interactions with other neuronal

assemblies to which it is functionally connected – metastability principle of brain-mind

functioning.53

By activating each other and being activated by external (or internal) requests, the

components form the system in which they collectively solve the required tasks and heading

toward the goal.

It is obvious that scalability of the above approach is limited by the size of the functional

module, the communication, and processing capacity in it. The OA framework offers a following

hierarchical approach to extend the scalability. Basically, the modules of the system at level n are

treated as members of a module of the higher level of abstractness n+1. At this level only those

functions (services) are presented, which could not be installed in the lower level modules. Such

architecture suggests a layered algorithm, where lower layers provide self-organizing properties

used by higher, more complex layers.

4.3. Implications for the artificial machine consciousness

The OA theory offers the plausible framework which states that whenever any pattern of

phenomenality (including reflective thought) is instantiated, there is neuro-physiological pattern

(revealed directly by EEG) of appropriate kind that corresponds to it.35,51 These neuron-

physiological patterns (expressed as the virtual operational modules) are brought to existence by

joint operations of many functional and transient neuronal assemblies in the brain.35,54 The

activity of neuronal assemblies is ‘hidden’ in the complex nonstationary structure of EEG.35,51

However, proper EEG analysis55 reveals the EEG architecture which is strikingly similar to the

architecture of a phenomenal world (see above).

If consciousness is a biological phenomenon in the brain30 realized by the highly organized

macro-level electrophysiological (EEG) phenomena (metastable OMs),35,51-54 the problem of

producing man-made ‘machine’ consciousness is the problem of duplicating the whole level of

architecture (with its inherent rules and mechanisms) found in EEG, which can constitute this

phenomenal level.91

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4.4. Implications for diagnosis of dynamic brain diseases

Recent research emphasizes the fact that majority of brain disorders and mental/psychiatric

problems are accompanied by disruption in the temporal structure of brain activity.92 From this

perspective, such disruption is viewed as a disorder of the metastable balance between large-

scale integration and independent processing in the brain, in favor of either independent or

hyper-ordered processing. In this context, it is important to study how different brain and mind

pathologies alter the temporal structure and metastable regimen of brain activity and how

different psychotropic drugs can modify temporal structure of brain activity in healthy subjects

and patients.

4.4.1. Depressive disorder

Depressed individuals tend to see even positive information as negative because it becomes

associated with personally relevant negative information.93 Additionally, an automatic,

unintentional use of negative social constructs in self-perception was reported in depressed

individuals.94

Analysis of EEG of depressed individuals showed that the particular spatial distribution of

functional connections and the sets of OMs reflect the metastable dysregulation between cortical

and subcortical systems, and the disordered representation of semantics that depressed

individuals tend to hold.95 It was found that the number and strength of short cortex functional

connections were significantly larger for the left than for the right hemisphere, while the number

and strength of long functional connections were significantly larger for the right than for the left

hemisphere. These findings were interpreted within the semantic framework, where an observed

increase in respected functional connections is adaptive, compensated, and is due to different

specializations of the left (monosemantic context) and right (polysemantic context) hemispheres,

both of which are functionally insufficient in patients with major depression.95 The ability to

form an adequate semantic context means the ability to integrate both mono- and polysemantic

contexts into a cohesive experience, thus helping subjects to experience themselves as ‘unity’

integrated into interrelations with other humans and the world. This integration is the most

important feature of the subject’s mental life and emotional balance.96 Distortion of such

integration results in changes in adaptive compensative mechanisms, with an increase in

functional connectivity being a particularly important one.

17

4.4.2. Opioid dependence

Given that repeated exposure to opiates initiates a significant reorganization of mental functions,

including those associated with drug-seeking and drug-taking behavior that characterize

addiction,97 we predict that systematic opioid exposure in abuse patients would result in a

significant reorganization of local and remote functional connectivity in the neocortex.98

It was found that acute use of opioids, together with the longitudinal opioid usage history,

results in the increase of local functional connectivity; and this was reflected in the formation of

large and stable neuronal assemblies.98 Therefore, the brain of opioid dependent patients cannot

reach a proper (for the healthy brain) resting state where individual brain areas, besides

expressing their own functioning, are also heavily involved in a collective activity (metastable

principle). Thus, such ‘normal’ resting brain state is alien to the neuron-cognitive profile of

‘addicted’ resting brain.

At the same time there was significant disruption in the remote functional synchrony among

neuronal assemblies in the brain of opioid addicts.98 Most likely, this disrupted brain remote

functional connectivity may constitute the candidate mechanism for a well-documented pattern

of phenomenal impairment in addicts that expressed as the lack of integration of different

cognitive functions for effective problem solving, deficits in abstract concept formation, set-

maintenance, set-shifting, behavioral control, and problems in the regulation of affect and

behavior.99-101

Altogether, these findings suggest that opioid dependence may be conceptualized as a new

metastable state around altered homeostatic levels in the brain.98

4.4.3. Opioid withdrawal

When addicts crave for drug, the anxiety, nervousness, lack of inhibitory control, positive drug

related expectancies, and intrusive thoughts related to drugs are simultaneously active in the

phenomenal mental world of addicts.102,103 Consistent with the OA framework, these complex

phenomenal states are critically based on the dynamical interactions between and within many

cortical neuronal assemblies responsible for such mental states.

It was predicted that such mental states would result in a significant increase of local and

remote functional cortical connectivity, where the dynamics of local brain operations (functions)

would be restrained by the large-scale context (removal of the aversive state) of mutually

18

connected cortical areas.104 Then it would explain the opioid withdrawal mental state: a strong

motivation for the excessive drug craving and drug related intrusive thoughts.

Experimental results (for detail, see Ref. 104) demonstrated significant increase in local and

remote cortical functional connectivity in the opioid-dependent patients during short-term

withdrawal period, thus suggesting that increased inter-area communication among cortical areas

may be one of the neurobiological underpinnings of the biased motivational and cognitive

processes such as attention, emotions, and memories during withdrawal103,105 supporting the

removal of the aversive state behavior. The restructuring of the operational synchrony process in

the brain of withdrawn addicts originates as a new altered metastable state around possible

homeostatic brain levels and is defined as adaptive process of achieving stability through change,

a metastability that is not within the normal homeostatic range.104

4.4.4. Schizophrenia

Schizophrenia is associated with impairments in pre-attentive sensory gating, perceptual

grouping, selective attention, working memory, and long-term memory (LTM), as well as with

phenomenal impairments usually interpreted in terms of context (hallucinations, delusions, and

appearance of incongruent associations).106-108 Such a broad range of contextual impairments is

exactly the type of general consequence to be expected from the failure of a common brain inter-

area coordination mechanism.109 It was suggested that such altered phenomenal world of

schizophrenics would be reflected in the disruption of the metastable balance between large-scale

integration and independent processing in the cortex, in favor of independent processing. Such

disruption is suggested as a contributing factor to the disorganization syndrome in

schizophrenia.109

OA studies provided evidence that in the brain of schizophrenic patients, there is shortening

of the life-span of transient neuronal assemblies, the size of neuronal assemblies decreased, and

their stability diminished.110 Simultaneously, there is reorganization of functional connectivity

among neuronal assemblies located in different cortical areas: such synchrony was reduced for

the long-distance brain areas and became stronger for the short-distance brain areas in

schizophrenic patients.111 These findings suggest a loss of dynamical balance between local,

specialized network functions and global integrative processes during schizophrenia. This

proposal offers the following interpretation: high ratings of cognitive disorganization

symptomatology in schizophrenics may be associated with a decrease in the long-distance

functional connectivity, while an increase in dynamical functional connections for the short-

19

distance unstable neuronal assemblies may underlie the inappropriate associations and reality

distortion.

4.4.5. Epilepsy

Considering that during chronic epilepsy patients experience altered mentality (so-called

‘epileptic personality’112,113), characterized by affective viscosity, egocentricity, obsessiveness,

and circumstantial thought, we hypothesize that the brain of such patients should produce

metastable OMs of larger size and longer life-span when compared with healthy subjects.

Study of epileptic patients confirms this supposition: OMs indeed tend to longer periods of

temporal stabilization and they involve more cortex areas than OMs in the brain of healthy

subjects.114 These findings suggest less dynamic performance of cooperative brain operations –

dynamic rigidity. As a result, such patients are less able to cope with the demands of a constantly

changing environment. Considering that interictal (in between the epileptic attacks) EEG in the

present study had not any signs of epileptiform abnormalities, observed mutual stabilization

between the types of spectral patterns in EEG channels independently on their correlation and

coherence is an inherent part of the mechanisms of ictogenesis which take place long before the

actual onset of a seizure (for detailed discussion, see Ref. 114).

4.4.6. Hypnosis

According to the mind-brain metastable continuum thesis (see Section 3), every event or change

at the mental (or cognitive) level must be accompanied by a corresponding change at the neural

level. Then it follows that if hypnosis involves a change in mental and cognitive mechanisms,

there must be a corresponding alteration taking place at the neural level. Furthermore, if change

on the neural level involves changes in the communication between different functional

operational modules in the brain, then they should be associated with changes in the underlying

EEG activity (synchrony between different brain areas). Thus, changes in the brain operational

architectonics during hypnosis may be regarded as a putative neuronal correlate of hypnosis.

Experimental research showed that during pure hypnosis115 as compared to the baseline

condition of consciousness, all studied parameters of functional connectivity were significantly

changed116: a) Delta-, beta-, and gamma-generated neuronal assemblies were characterized by

decreased size and decreased stability, being indicative of an increased independence of brain

processes (effort to maintain a state of alertness) in the hypnotic condition. Large and stable

20

alpha- and theta-generated neuronal assemblies, which appeared during hypnosis, indicate that

the subject was more facilitated to process information than in the baseline condition; b)

Practically all cortical locations demonstrated decrease in functional connections and decrease in

their strength, signifying the enhancement of independent processes in the brain and disruption

of the integrated brain functioning.

4.4.7. Psychotropic drugs

Elsewhere we propose that the future of psychopharmacology lies in its ability to design the

psychotropic drugs which can restore the normal temporal and metastable structure of brain

activity.92 Research studies conform the idea that psychotropic drugs can alter or restore the

natural metastable organization of brain activity.

In the lorazepam (benzodiazepine) study it has been shown that in healthy subjects under the

lorazepam administration the number of functional connections in the cortex and the strength of

these connections increased significantly; and that these changes in the brain operational

archirtectonics were paralleled by such mental changes as slowing of thinking and cognition.81

In the methadone (used as a maintenance treatment for opioid dependent patients) study it

was found that methadone could actually restore the normal cortical metastable balance in opioid

addicts after at least six-month methadone treatment; and this was paralleled by a decrease of

aggressive, dependent, and depressive mind states (for a detailed discussion, see Ref. 117).

The data reviewed above illustrate that psychotropic drugs can modify the metastable

organization of brain functioning.

5. Conclusion

The intuitive understanding that what happens in the mind depends upon what happens in the

brain and vice-versa goes back for at least two millennia. Already ancient doctors or hillers had

observed that injuries to the brain at times resulted in profound changes in the personality and

mental life of a person.

Many years of careful research in neuroscience prepared the basis for a conclusion which we

stressed in the current contribution, – that brain and mental processes are best seen as two

aspects of one unified, but metastable continuum. Based on the evidence presented here we may

regard brain and mental architectures as complementary physiological (inner) and

phenomenological (outer) aspects of one complex set of events that together constitute

21

metastable brain-mind agency. In this context, the hierarchy of phenomenal structures (features,

patterns, objects, scenes) has its electrophysiological equivalent in an operational hierarchy of

neuronal assemblies and OMs, which correspond to the phenomenal entities.

References

1. W. J. Freeman, Mass Action in the Nervous System (Academic Press, New York, 1975). 2. W. J. Freeman, Neurodynamics (Springer, New York, 2000). 3. W. J. Freeman, The wave packet: an action potential for the 21st century, J. Integr.

Neurosci. 2(2003) 3-30. 4. W. J. Freeman, Origin, structure and role of background EEG activity, Clin. Neurophysiol.

116(2004) 1117-1129. 5. W. J. Freeman, A field-theoretic approach to understanding scale-free neocortical dynamics,

Biol. Cybern. 92(2005) 350-359. 6. W. J. Freeman, A pseudo-equilibrium thermodynamic model of information processing in

nonlinear brain dynamics, Neural Netw. 21(2008) 257-265. 7. R. Kozma and W. J. Freeman, Classification of EEG patterns using nonlinear dynamics and

identifying chaotic phase transitions, Neurocomputing 44(2002) 1107-1112. 8. W. J. Freeman and J. M. Barrie, Chaotic oscillations and the genesis of meaning in cerebral

cortex, in Temporal Coding in the Brain, eds. G. Buzsaki, R. Llinas, W. Singer, A. Berthoz and Y. Christen (Springer-Verlag, Berlin Heidelberg, 1994), pp. 13-37.

9. W. J. Freeman and M. D. Holmes, Metastability, instability, and state transitions in neocortex, Neural Netw. 18(2005) 497-504.

10. W. J. Freeman and G. Vitiello, Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics, Phys. Life Rev. 3(2006) 93-118.

11. G. Werner, Metastability, criticality and phase transitions in brain and its models, Biosystems 90(2007) 496-508.

12. W. J. Freeman, Nonlinear dynamics of paleocortex manifested in the olfactory EEG, Biol. Cybern. 35(1979) 21–37.

13. W. J. Freeman and W. Schneider, Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors, Psychophysiology 19(1982) 44–56.

14. W. J. Freeman, Simulation of chaotic EEG patterns with a dynamic model of the olfactory system, Biol. Cybern. 56(1987) 139–150.

15. R. Kozma and W. J. Freeman, Basic principles of the KIV model and its application to the navigation problem, J. Integrative Neurosci. 2(2003) 125-140.

16. R. Kozma, W. J. Freeman and P. Erdi, The KIV model – nonlinear spatio-temporal dynamics of the primordial vertebrate forebrain, Neurocomputing 52-54(2003) 819-825.

17. W. J. Freeman, Indirect biological measures of consciousness from field studies of brains as dynamical systems, Neural Netw. 20(2007) 1021–1031.

18. S. L. Bressler, The formation of global neurocognitive state, in Neurodynamics of Cognition and Consciousness, eds. L. I. Perlovsky and R. Kozma (Springer, Heidelberg, 2007), pp. 61-72.

19. J. A. S. Kelso and E. Tognoli, Toward a complementary neuroscience: metastable coordination dynamics of the brain, in Neurodynamics of Cognition and Consciousness, eds. L. I. Perlovsky and R. Kozma (Springer, Heidelberg, 2007), pp. 39-59.

20. S. L. Bressler and J. A. S. Kelso, Cortical coordination dynamics and cognition, Trends Cogn. Sci. 5(2001) 26-36.

22

21. J. A. S. Kelso, Dynamic Patterns: The Self-Organization of Brain and Behavior (MIT Press, Cambridge MA, 1995).

22. J. A. S. Kelso and D. Engstrøm, The Complementary Nature (MIT Press, Cambridge, 2006).

23. D. Moran, Introduction to Phenomenology (Routledge, London and New York, 2000). 24. Stanford Encyclopedia of Philosophy, Phenomenology (2008),

http://plato.stanford.edu/entries/phenomenology/ 25. W. James, The Principles of Psychology, Vol. I (Dover, New York, 1890). 26. B. B. Mangan, Taking phenomenology seriously: the “fringe” and its implications for

cognitive research, Conscious. Cogn. 2(1993a) 89-108. 27. B. B. Mangan, Some philosophical and empirical implications of the fringe, Conscious.

Cogn. 2(1993b) 142-154. 28. W. L. Chafe, Discourse, Consciousness, and Time: The Flow and Displacement of

Conscious Experience in Speaking and Writing (Univ. of Chicago Press, Chicago, 1994). 29. D. Galin, The structure of awareness: contemporary applications of William James’

forgotten concept of “the fringe”, J. Mind Behav. 15(1994) 375-402. 30. A. Revonsuo, Inner Presence: Consciousness as a Biological Phenomenon (MIT Press,

Cambridge, MA, 2006). 31. G. M. Edelman and G. Tononi, A Universe of Consciousness: How Matter Becomes

Imagination (Basic Books, New York, 2000a). 32. S. Pockett, The nature of consciousness: A hypothesis (Writers Club Press, Lincoln NE,

2000). 33. A. Revonsuo, Can functional brain imaging discover consciousness in the brain? J.

Conscious. Stud. 8(2001) 3-23. 34. J. McFadden, Synchronous firing and its influence on the brain’s electromagnetic field.

Evidence for an electromagnetic field theory of consciousness, J. Conscious. Stud. 9(2002) 23–50.

35. An. A. Fingelkurts and Al. A. Fingelkurts, Mapping of the brain operational architectonics, in Focus on Brain Mapping Research, ed. F. J. Chen (Nova Science Publishers, Inc, 2005), pp. 59–98, http://www.bm-science.com/team/chapt3.pdf

36. P. O. Haikonen, Robot brains: Circuits and Systems for Conscious Machines (Wiley and Sons, UK, 2007).

37. B. J. Baars, A Cognitive Theory of Consciousness (Cambridge University Press, Cambridge, 1988).

38. B. J. Baars, How does a serial, integrated, and very limited stream of consciousness emerge from a nervous system that is mostly unconscious, distributed, parallel, and of enormous capacity? in Essential Sources in the Scientific Study of Consciousness, ed. B. J. Baars, W. P. Banks and J. B. Newman (MIT Press, Cambridge, 2003).

39. F. Crick and C. Koch, Toward a neurobiological theory of consciousness, Semin. Neurosci. 2(1990) 263-725.

40. F. Crick and C. Koch, A framework for consciousness, Nat. Neurosci. 6(2003) 119-126. 41. A. R. Damasio, Descartes’ Error: Emotion, Reason and the Human Brain (Avon Books,

New York, 1994). 42. A. R. Damasio, A neurobiology for consciousness, in Neural Correlates of Consciousness,

ed. T. Metzinger (MIT Press, Cambridge, MA, 2000). 43. G. M. Edelman and G. Tononi, Reentry and the dynamic core: neural correlates of

conscious experience, in Neural Correlates of Consciousness, ed. T. Metzinger, (MIT Press, Cambridge, MA, 2000b).

44. S. Zeki, The disunity of consciousness, Trends Cogn. Sci. 7(2003) 214-218.

23

45. S. Zeki, Insights into visual consciousness, in Human Brain Function, ed. R. S. J. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, S. Zeki, J. Ashburner and W. Penny (Academic Press, San Diego, 2004).

46. F. J. Varela, Resonant cell assemblies: A new approach to cognitive functions and neuronal synchrony, Biol. Res. 28(1995) 81-95.

47. T. Metzinger (ed.), Neural Correlates of Consciousness: Empirical and Conceptual Questions (The MIT Press, Cambridge, MA, 2000).

48. G. Rees, G. Kreiman and C. Koch, Neural correlates of consciousness in humans, Nat. Rev. Neurosci. 3(2002) 261-270.

49. A. Noë and E. Thompson, Are there neural correlates of consciousness? J. Conscious. Stud. 11(2004) 3–28.

50. A. K. Seth, Z. Dienes, A. Cleeremans, M. Overgaard and L. Pessoa, Measuring consciousness: relating behavioural and neurophysiological approaches, Trends Cogn. Sci. 2(2008) 73-93.

51. An. A. Fingelkurts and Al. A. Fingelkurts, Operational architectonics of the human brain biopotential field: towards solving the mind-brain problem, Brain Mind 2(2001) 261–296, http://www.bm-science.com/team/art18.pdf

52. An. A. Fingelkurts and Al. A. Fingelkurts, Operational architectonics of perception and cognition: a principle of self-organized metastable brain states (invited full-text contribution), VI Parmenides Workshop, Institute of Medical Psychology, Elba/Italy, (April 5–10, 2003), http://www.bm-science.com/team/art24.pdf.

53. An. A. Fingelkurts and Al. A. Fingelkurts, Making complexity simpler: multivariability and metastability in the brain, Int. J. Neurosci. 114(2004) 843–862.

54. An. A. Fingelkurts and Al. A. Fingelkurts, Timing in cognition and EEG brain dynamics: discreteness versus continuity, Cogn. Process. 7(2006) 135–162.

55. An. A. Fingelkurts and Al. A. Fingelkurts, Brain-mind Operational Architectonics imaging: technical and methodological aspects, Open Neuroimag. J. 2(2008) 73-93.

56. A. R. McIntosh, S. M. Fitzpatrick and K. J. Friston, On the marriage of cognition and neuroscience, NeuroImage 14(2001) 1231-1237.

57. F. Varela, J.-P. Lachaux, E. Rodriguez and J. Martinerie, The brainweb: phase synchronization and large-scale integration, Nat. Rev. Neurosci. 2(2001) 229-239.

58. E. R. John, The neurophysics of consciousness, Brain Res. Brain Res. Rev. 39(2002) 1-28. 59. E. Basar, C. Basar-Eroglu, S. Karakas and M. Schurmann, Gamma, alpha, delta, and theta

oscillations govern cognitive processes, Int. J. Psychophysiol. 39(2001) 241-248. 60. P. L. Nunez, Toward a quantitative description of large-scale neocortical dynamic function

and EEG. Behav. Brain Sci. 23(2000) 371-398. 61. A. Ya. Kaplan and S. L. Shishkin, Application of the change-point analysis to the

investigation of the brain’s electrical activity, in Nonparametric Statistical Diagnosis: Problems and Methods, eds. B. E. Brodsky and B. S. Darkhovsky (Kluwer Academic Publishers, Dordrecht, the Netherlands, 2000), pp. 333-388.

62. A. Ya. Kaplan, Nonstationary EEG: Methodological and experimental analysis, Usp. Fiziol. Nauk. (Success in Physiological Sciences) 29(1998) 35-55 (in Russian).

63. E. R. John, A field theory of consciousness, Conscious. Cogn. 9-10(2001) 184-213. 64. L. A. Shapiro, Multiple realizations. J. Phil. 97(2000) 635-654. 65. An. A. Fingelkurts, Al. A. Fingelkurts and C. F. H. Neves, Functional isomorphism between

Operational Architectonics of brain functioning and subjective phenomenology. International Conference – Coordination Dynamics: Coordination: Neural, Behavioral and Social Dynamics (Boca Raton, Florida, February 22-25, 2007).

66. Krippendorff's Dictionary of Cybernetics, in Web Dictionary of Cybernetics and Systems, ed. F. Heylighen (1989), http://pespmc1.vub.ac.be/ASC/OPERATION.html

24

67. Collins Essential English Dictionary, 2nd Edition (HarperCollins Publishers, 2006). 68. V. Makarenko and R. Llinas, Experimentally determined chaotic phase synchronization in a

neuronal system. Proc. Natl. Acad. Sci. U.S. A. 95(1998) 15747-15752. 69. J. Triesch, C. von der Malsburg, Democratic integration: Self-organized integration of

adaptive cues, Neural Comput. 13(2001) 2049-2074 70. M. Abeles, Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press,

Cambridge, 1991). 71. W. Singer, Synchronization of cortical activity and its putative role in information

processing and learning, Annu. Rev. Physiol. 55(1993) 349-374. 72. C. von der Malsburg, The what and why of binding: the modeler’s perspective, Neuron

24(1999) 95-104. 73. G. Palm, Cell assemblies as a guideline for brain research, Concepts Neurosci. 1(1990) 133-

147. 74. H. Eichenbaum, Thinking about brain cell assemblies, Science 261(1993) 993-994. 75. A. Ya. Kaplan and S. V. Borisov, Dynamic properties of segmental characteristics of EEG

alpha activity in rest conditions and during cognitive load, Zh. Vyssh. Nerv. Deiat. Im I.P. Pavlova (IP Pavlov Journal of Higher Nervous Activity) 53(2003) 22-32 (in Russian)

76. A. Ya. Kaplan, An. A. Fingelkurts, Al. A. Fingelkurts, S. V. Borisov, B. S. Darkhovsky, Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges, Signal Process. 85(2005) 2190-2212.

77. W. J. Freeman and G. Vitiello, Nonlinear brain dynamics and many-body field dynamics, Electromagn. Biol. Med. 24(2005) 233–241.

78. J. A. S. Kelso, Behavioral and neural pattern generation: the concept of Neurobehavioral Dynamical System (NBDS), in Cardiorespiratory and Motor Coordination, ed. H. P. Koepchen (Springer-Verlag, Berlin, 1991).

79. E. Basar, Macrodynamics of electrical activity in the whole brain, Int. J. Bifurcat. Chaos 14(2004) 363-381.

80. An. A. Fingelkurts, Time-spatial Organization of Human EEG Segment’s Structure, PhD Dissertation, (Moscow State University, Moscow, Russian Federation, 1998), pp. 415 (in Russian).

81. An. A. Fingelkurts, Al. A. Fingelkurts, R. Kivisaari, E. Pekkonen, R. J. Ilmoniemi, S. A. Kähkönen, Local and remote functional connectivity of neocortex under the inhibition influence, NeuroImage 22(2004) 1390-1406

82. An. A. Fingelkurts, Al. A. Fingelkurts, C. M. Krause, R. Möttönen and M. Sams, Cortical operational synchrony during audio-visual speech integration, Brain Language 85(2003a) 297-312.

83. An. A. Fingelkurts, Al. A. Fingelkurts, C. M. Krause, A. Ya. Kaplan, S. V. Borisov and M. Sams, Structural (operational) synchrony of EEG alpha activity during an auditory memory task, NeuroImage 20(2003b) 529-542.

84. W. Singer, Consciousness and the binding problem, in Cajal and Consciousness: Scientific Approaches to Consciousness on the Centential of Ramon y Cajal’s Textura, Vol. 929, ed. E. P. Marijuan (Annals of the NYAS, New York: New York, 2001), pp. 123-146.

85. G. O'Brien and J. Opie, Putting content into a vehicle theory of consciousness, Behav. Brain Sci. 22(1999) 175-196.

86. H. McGurk and J. W. MacDonald, Hearing lips and seeing voices, Nature 264(1976) 746-748.

87. G. Benedetti, Operational Noology as a new methodology for the study of thought and language: theoretical aspects and possible practical applications, Cogn. Process. 7(2006) 217-243.

88. G. Marchetti, A presentation of Attentional Semantics. Cogn. Process. 7(2006) 163-194.

25

89. G. Benedetti, G. Marchetti, Al. A. Fingelkurts, An. A. Fingelkurts, Mind operational semantics and brain Operational Architectonics: a putative correspondence. Journal of Biological Physics, Special Issue: Boundaries of Brain Information Processing (2009) in press.

90. E. Burmakin, Al. A. Fingelkurts and An. A. Fingelkurts, Self-organization of dynamic distributed systems applying principles of integrative activity of brain neuronal assemblies. Algorithms, Special Issue: Neural Networks and Sensors (2008) in press.

91. An. A. Fingelkurts, Al. A. Fingelkurts, C. F. H. Neves, Brain and mind Operational Architectonics and man-made ‘machine’ consciousness, Cogn. Process. 2008, ahead of print, DOI 10.1007/s10339-008-0234-y.

92. An. A. Fingelkurts, Al. A. Fingelkurts, S Kähkönen, New perspectives in pharmaco-electroencephalography, Prog. Neuropsychopharmacol. Biol. Psychiatry 29(2005) 193-199.

93. G. J. Siegle, A neural network model of attention biases in depression, in Disorders of Brain, Behavior, and Cognition: The Neurocomputational Perspective, eds. J. Reggia and E. Ruppin (Elsevier, New York, NY, 1999), pp. 415–441.

94. J. A. Bargh and M. E. Tota, Context-dependent automatic processing in depression: accessibility of negative constructs with regard to self but not others, J. Pers. Soc. Psychol. 54(1988) 925–939.

95. An. A. Fingelkurts, Al. A. Fingelkurts, H. Rytsälä, K. Suominen, E. Isometsä and S. Kähkönen, Impaired functional connectivity at EEG alpha and theta frequency bands in major depression, Hum. Brain Mapp. 28(2007a) 247-261.

96. V. S. Rotenberg, The peculiarity of the right-hemisphere function in depression: solving the paradoxes, Prog. Neuro-Psychophar. Biol. Psychiat. 28(2004) 1–13.

97. T. E. Robinson and K. C. Berridge, The psychology and neurobiology of addiction: an incentive-sensitization view, Addiction 95(2000) S91–S117.

98. An. A. Fingelkurts, Al. A. Fingelkurts, R. Kivisaari, T. Autti, S. Borisov, V. Puuskari, O. Jokela and S. Kähkönen, Increased local and decreased remote functional connectivity at EEG alpha and beta frequency bands in opioid-dependent patients, Psychopharmacology 188(2006) 42–52.

99. L. Miller, Neuropsychodynamics of alcoholism and addiction: personality, psychopathology, and cognitive style, J. Subst. Abuse Treat. 7(1990) 31-49.

100. T. J. Ornstein, J. L. Iddon, A. M. Baldacchino, B. J. Sahakian, M. London, B. J. Everitt and T. W. Robbins, Profiles of cognitive dysfunction in chronic amphetamine and heroin abusers, Neuropsychopharmacology 23(2000) 113-126.

101. P. E. Davis, H. Liddiard and T. M. McMillan, Neuropsychological deficits and opiate abuse, Drug Alcohol Depend. 67(2002) 105–108.

102. T. J. De Vries and T. S. Shippenberg, Neural systems underlying opiate addiction, J. Neurosci. 22(2002) 3321-3325.

103. I. H. A. Franken, Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches, Prog. Neuropsychopharmacol. Biol. Psychiatry 27(2003) 563-579.

104. An. A. Fingelkurts, Al. A. Fingelkurts, R. Kivisaari, T. Autti, S. Borisov, V. Puuskari, O. Jokela and S. Kähkönen, Opioid withdrawal results in an increased local and remote functional connectivity at EEG alpha and beta frequency bands, Neurosci. Res. 58(2007b) 40–49.

105. D. C. Drummond, Theories of drug craving, ancient and modern, Addiction 96(2001) 33-46. 106. J. A. Gray, J. Feldon, J. N. P. Rawlins, D. R. Hemsley and A. D. Smith, The

neuropsychology of schizophrenia, Behav. Brain Sci. 14(1991) 1-84. 107. D. R. Hemsley, J. N. P. Rawlins, J. Feldon, S. H. Jones and J. A. Gray, The

neuropsychology of schizophrenia: Act 3, Behav. Brain Sci. 16(1993) 209-215.

26

108. A. D. Pickering, Schizophrenia: In context or in the garbage can? Behav. Brain Sci. 16(1993) 205-206.

109. S. Bressler, Cortical coordination dynamics and the disorganization syndrome in schizophrenia, Neuropsychopharmacology 28(2003) S35–S39.

110. S. Borisov, A. Ya. Kaplan, N. L. Gorbachevskaya and I. A. Kozlova, Segmental structure of the EEG alpha activity in adolescents with disorders of schizophrenic spectrum, Zh. Vyssh. Nerv. Deiat. Im I. P. Pavlova (IP Pavlov Journal of Higher Nervous Activity) 55(2005a) 329-335 (in Russian).

111. S. V. Borisov, A. Ya. Kaplan, N. L. Gorbachevskaia and I. A. Kozlova, Analysis of EEG structural synchrony in adolescents suffering from schizophrenic disorders, Fiziol. Chelov. (Human Physiology) 31(2005b) 16-23 (in Russian).

112. S. G. Waxman and N. Geschwind, The interictal behavior syndrome in temporal lobe epilepsy, Arch. Gen. Psychiatry 32(1975) 1580-1586.

113. D. M. Bear and P. Fedio, Quantitative analysis of interictal behavior in temporal lobe epilepsy, Arch. Neurol. 34(1977) 454-467.

114. Al. A. Fingelkurts, An. A. Fingelkurts and A. Ya. Kaplan, Interictal EEG as a physiological adaptation. Part II: Topographic variability of composition of brain oscillations in interictal EEG, Clin. Neurophysiol. 117(2006) 789–802.

115. W. E. Jr. Edmonston, The effects of neutral hypnosis on conditioned responses: implications for hypnosis as relaxation, in Hypnosis: Developments in Research and New Perspectives, eds. E. Fromm and R. E. Shor (Aldine, New York, 1979), pp. 415–455.

116. An. A. Fingelkurts, Al. A. Fingelkurts, S. Kallio and A. Revonsuo, Cortex functional connectivity as a neurophysiological correlate of hypnosis: an EEG case study, Neuropsychologia 45(2007) 1452-1462.

117. An. A. Fingelkurts, Al. A. Fingelkurts, R. Kivisaari, T. Autti, S. Borisov, V. Puuskari, O. Jokela and S. Kähkönen, Methadone restores local and remote EEG functional connectivity in opioid-dependent patients. Int. J. Neurosci. (2008), in press.